System and method for processing data signals

Abstract

A signal processing method include steps initializing a residual data signal representative of an acquired data signal, determining a significant coefficient corresponding to the residual data signal, updating the residual data signal using the significant coefficient to generate updated residual data signal, iteratively determining significant coefficients to generate a plurality of significant coefficients using the updated residual data signal, updating the plurality of significant coefficients by using a successive approximation technique, to improve the numerical accuracy of the significant coefficients and reconstructing a data signal using the updated plurality of significant coefficients.

Description

BACKGROUND[0001]Embodiments of the invention relate generally to a field of processing data signals, and more specifically to reducing the number of data samples required for image / signal reconstruction.[0002]With advances in various areas of technology, such as, but not limited to, imaging, networking, healthcare, audio, video entertainment and commmunication, huge volumes of data are frequently generated. More particularly, in imaging and healthcare applications, it may be desirable to acquire several images of one or more patients and subsequently store these images, thereby entailing use of considerable storage space and processing time. Similarly, communication applications call for reductions in bandwidth and an increase in data transmission speed to communicate data. Traditionally, data compression techniques have been employed to aid in the efficient storage of such data. Data compression may entail encoding information using fewer bits (or other information-bearing units) t...

AI Technical Summary

Benefits of technology

[0007]Briefly in accordance with one aspect of the technique a signal processing method is provided. The method provides for initializing a residual data signal representative of an acquired data signal, determining a significant coefficient corresponding to the residual data signal, updating the residual data signal using the significant coefficient to generate updated residual data signal, iteratively determining significant coefficients to generate a plurality of significant coefficients using the updated residual data signal, updating the plurality of significant coefficients by using a successive approximation technique to improve the numerical accuracy of the significant coefficients, and reconstructing a data signal using the updated plurality of significant coefficients. Systems and apparatus that afford functionality of the type defined by this method may be provided by the present technique.

[0008]In accordance with a further aspect of the present technique a system is provided. The system includes an acquisition subsystem configured to obtain data from an object and a processing subsystem in operational communication with the data acquisition subsystem. The processing subsystem further comprises a signal processing platform configured to initialize a residual data signal representative of an acquired data signal, determine a significant coefficient corresponding to the residual data signal, update the residual data signal using the significant coefficient to generate an updated residual data signal, iteratively determine significant coefficients to generate a plurality of significant coefficients using the updated residual data signal, update the plurality of significant coefficients by using a successive approximation technique, to improve the numerical accuracy of the significant coefficients, and reconstruct a data signal using the updated plurality of significant coefficients.

Problems solved by technology

Furthermore, compressed sensing reduces the number of data measurements required for image / signal reconstruction.

However, capturing and processing data related to the underlying subject involve laborious and time-consuming processes.

By way of example, performing a Magnetic Resonance Imaging (MRI) scan of a patient, performing a three-dimensional (3D) CT scan of a patient, measuring a 3D nuclear magnetic resonance spectrum, and conducting a 3D seismic survey typically entail time-consuming processes.

Also, if an assumption about the compressible nature of the images is made, the methods used for image reconstruction may require substantial processing time.

As the number of selected elements increases, the QR decomposition step takes significant computational time.

Also, all the forward projected data wavelets need to be stored, thereby increasing demands on storage means.

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